An electric power plant, a chemical plant, a steel plant, a water supply and sewage plant, or the like introduces a control system for controlling a process in a plant. Likewise, a facility such as a building or a factory introduces a control system for controlling air conditioning, electricity, illumination, water supply and drainage, and so on. Meanwhile, a logging system for recording a state of an appliance is equipped in an appliance for a line in a factory, a motorcar, and a railroad car to recognize a state of an appliance.
In these systems, various types of time-series data obtained by a sensor installed at the appliance as time elapses are accumulated.
The time-series data is analyzed as follows.
Partial time-series data extracted from recent time-series data, namely, test time-series data is compared with partial time-series data extracted from past time-series data, namely, training time-series data and then, partial time-series data of the test time-series data similar to the partial time-series data of the training time-series data is searched for. Subsequently, among the partial time-series data of the test time-series data similar thereto, partial time-series data of the test time-series data least similar to the partial time-series data of the training time-series data is detected as a singularity. Thereafter, an abnormality of the appliance is detected through the detection of the singularity.
The partial time-series data is data extracted from the time-series data to have a length corresponding to a width of a sliding time window and called a segment.
A segment of the training time-series data having a distance closest to a segment of the test time-series data is extracted with respect to each of the segments of the test time-series data and then, a segment of the test time-series data having a distance farthest from the segment of the training time-series data is regarded as the singularity. A Euclidean distance and a DTW distance are widely utilized as types of the distance between segments. The DTW stands for Dynamic Time Warping.
Here, the aforementioned approach for detecting the singularity is called a simple approach.
Because distances of all combinations of the segments of the training time-series data and the segments of the test time-series data are found out, the simple approach has a considerably increased amount of computation.
Non Patent Literature 1 has proposed an approach for reducing the amount of computation by discontinuing the computation of the distances by way of an indicator having a less amount of computation than the case of the computation of the distances between the segments.
Non Patent Literature 2 has proposed an approach for reducing the amount of computation by comparing the segments of the test time-series data with a sample segment. The sample segment is called an Exemplar and generated from the training time-series data by integrating similar segments thereof.
In a case where the number of sample segments can be sufficiently narrowed down, the approach according to Non Patent Literature 2 can reduce the amount of computation more considerably than the case of the simple approach and the approach according to Non Patent Literature 1.
However, the approach according to Non Patent Literature 2 is an approach for finding out an approximate solution by sampling the training time-series data and accordingly, an error thereof, namely, an approximation error needs to be taken into account.
In the approach according to Non Patent Literature 2, as long as a test segment having a distance to the sample segment equal to or less than a threshold is present, the union of this test segment with the sample segment is repeated. As a result, the approximation error increases and the approximation error cannot be ensured to fit within an allowable range.
Besides, it is also not possible to find out the approximation error contained in the sample segment by way of quantification, an error contained in a search result cannot be evaluated as well.
Non Patent Literature 3 has disclosed the degree of complexity as a feature quantity used in the judgment of an inter-segment distance relative to the threshold.